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This work introduces RecreationWorld, a five-platform framework built around recreation: given a running reference, an agent must discover its behavior and build a faithful implementation with no prescribed workflow, and introduces RecreationBench, comprising 250 diverse tasks across domains and platforms.
The findings reveal that predictive learning objectives impose strong regularities on representation geometry, suggesting a lightweight path to interoperability among decentralized vision systems.
EvoCUA-1.5 extends self-evolving computer-use agents from offline experience learning to online reinforcement learning, where policies interact with executable sandbox environments and improve from verifiable task outcomes and provides a practical framework for scaling online RL in multi-turn computer-use agents.
Activation Steering Adapter (ASA), a training-free, inference-time controller that performs a single-shot mid-layer intervention and targets tool domains via a router-conditioned mixture of steering vectors with a probe-guided signed gate to amplify true intent while suppressing spurious triggers is proposed.
Qwen-CUA is introduced, a native computer-use agent with a 397B-A17B Qwen mixture-of-experts backbone that outperforms Qwen3.7 and remains competitive with leading proprietary systems, and scalable verifiable interaction and hybrid tool use as key directions.
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